How to use it to ‘extract other graphs conforming to SEGN standards’

A reusable SEGN standard ‘parent-child’ generation paradigm is now established:

Employ the lossless parent graph (Level 7) as the ‘single source of truth’ (where any semantics can be traced back to the original text).

Employ derived semantic graphs (concept/mechanism/event + edges) as the ‘computable reasoning layer’.

Utilise Evidence Alignment as SEGN's ‘audit interface’:

Any concept/mechanism/causal edge → can be traced back to its source evidence node (source_node_id + source_text).

When establishing a SEGN standard graph for new documents, simply reuse the same field structure and alignment rules:

First generate the Level 7 lossless master graph

Then regenerate the derived semantic graph

Finally regenerate the evidence alignment table

Together, these form a transferable, auditable, and scalable SEGN standardised data product.


如何用它去“提取其他符合 SEGN 标准的图谱”

现在已经具备一个可复用的 SEGN 标准“母-子”生成范式：

用 无损母图谱（7级） 做“唯一事实源”（任何语义都能回指原文）。

用 派生语义图谱（concept/mechanism/event + edges） 做“可计算推理层”。

用 Evidence Alignment 作为 SEGN 的“审计接口”：

任意概念/机制/因果边 → 都能定位到原文证据节点（source_node_id + source_text）。

当你要对新文档建立 SEGN 标准图谱时，只需复用同一套字段结构与对齐规则：

先生成 7 级无损母图谱

再生成派生语义图谱

再生成证据对齐表

三者一起就是 可迁移、可审计、可扩展 的 SEGN 标准化数据产品